# Example document classification pipeline
class DocumentClassifier:
    def __init__(self):
        self.categories = {
            'corporate': ['articles', 'bylaws', 'minutes', 'resolutions'],
            'financial': ['statements', 'audit', 'tax', 'financing'],
            'employment': ['collective', 'policy', 'compliance', 'hr'],
            'ip_tech': ['patent', 'trademark', 'license', 'technical']
        }

    def classify_document(self, filename, content_sample):
        # Simple keyword-based classification
        # In production, use ML classification
        filename_lower = filename.lower()

        for category, keywords in self.categories.items():
            if any(keyword in filename_lower for keyword in keywords):
                return category

        return 'general'

    def get_terminology_db(self, category):
        # Route to specialized terminology database
        return f"terminology_{category}.json"

import asyncio
from concurrent.futures import ThreadPoolExecutor

class TranslationPipeline:
    def __init__(self, max_workers=10):
        self.executor = ThreadPoolExecutor(max_workers=max_workers)
        self.shared_glossary = {}
        self.processed_docs = {}

    async def process_document_batch(self, documents):
        # Classify documents first
        classified_docs = self.classify_batch(documents)

        # Process each category in parallel
        tasks = []
        for category, doc_list in classified_docs.items():
            task = asyncio.create_task(
                self.process_category_batch(category, doc_list)
            )
            tasks.append(task)

        results = await asyncio.gather(*tasks)
        return self.merge_results(results)

    async def process_category_batch(self, category, documents):
        # Load category-specific terminology
        terminology_db = self.load_terminology(category)

        # Process documents in parallel within category
        loop = asyncio.get_event_loop()
        futures = [
            loop.run_in_executor(
                self.executor, 
                self.translate_document, 
                doc, 
                terminology_db
            )
            for doc in documents
        ]

        return await asyncio.gather(*futures)

class TerminologyManager:
    def __init__(self):
        self.global_glossary = {}
        self.category_glossaries = {}
        self.entity_names = set()

    def extract_entities(self, document_text, doc_type):
        """Extract company names, deal terms, etc."""
        import re

        # Extract likely company names (simplified)
        company_pattern = r'\b[A-Z][a-zA-Z\s]+(?:Inc|Corp|Ltd|LLC|GmbH|S\.A\.|Ltda)\.?\b'
        companies = re.findall(company_pattern, document_text)

        for company in companies:
            self.entity_names.add(company.strip())

        return companies

    def update_glossary(self, source_term, target_term, category=None):
        """Update terminology database with new translations"""
        self.global_glossary[source_term] = target_term

        if category:
            if category not in self.category_glossaries:
                self.category_glossaries[category] = {}
            self.category_glossaries[category][source_term] = target_term

    def get_translation(self, term, category=None):
        """Get consistent translation for a term"""
        # Check category-specific glossary first
        if category and category in self.category_glossaries:
            if term in self.category_glossaries[category]:
                return self.category_glossaries[category][term]

        # Fall back to global glossary
        return self.global_glossary.get(term, None)

import hashlib
import json
from datetime import datetime

class SecureTranslationPipeline:
    def __init__(self, encryption_key):
        self.encryption_key = encryption_key
        self.audit_log = []

    def process_secure_document(self, document, user_id, project_id):
        # Log document access
        doc_hash = hashlib.sha256(document.content.encode()).hexdigest()

        audit_entry = {
            'timestamp': datetime.utcnow().isoformat(),
            'user_id': user_id,
            'project_id': project_id,
            'document_hash': doc_hash,
            'action': 'translation_started'
        }

        self.audit_log.append(audit_entry)

        # Process with encryption in transit
        encrypted_content = self.encrypt_content(document.content)
        result = self.translate_encrypted(encrypted_content)

        # Log completion
        audit_entry['action'] = 'translation_completed'
        audit_entry['timestamp'] = datetime.utcnow().isoformat()
        self.audit_log.append(audit_entry)

        return result

    def encrypt_content(self, content):
        # Implement proper encryption here
        # This is a placeholder
        return content

    def auto_delete_after_project(self, project_id, retention_days=0):
        """Automatically delete project data after completion"""
        # Implementation for automatic cleanup
        pass

class VDRIntegration:
    def __init__(self, vdr_api_key, vdr_endpoint):
        self.api_key = vdr_api_key
        self.endpoint = vdr_endpoint

    async def monitor_new_documents(self, project_id):
        """Poll VDR for new documents requiring translation"""
        while True:
            new_docs = await self.check_for_new_documents(project_id)

            if new_docs:
                await self.trigger_translation_pipeline(new_docs)

            await asyncio.sleep(300)  # Check every 5 minutes

    async def upload_translated_document(self, original_doc_id, translated_content):
        """Upload translated version back to VDR"""
        # Implementation depends on VDR API
        pass

class PipelineMetrics:
    def __init__(self):
        self.metrics = {
            'documents_processed': 0,
            'avg_processing_time': 0,
            'terminology_consistency_rate': 0,
            'quality_scores': []
        }

    def calculate_consistency_rate(self, translated_docs):
        """Check terminology consistency across documents"""
        # Implementation for measuring consistency
        pass

    def export_metrics_for_reporting(self):
        return json.dumps(self.metrics, indent=2)